Data Science for Mental Health (DS4MH) @ The Alan Turing Institute
About Us
The vision for this interest group is to kick-start one or more projects using contemporary data science and multi-modal data for mental health to provide insight and benefit for individuals, clinicians, and contribute to fundamental research in mental health (including dementia) as well as the data science methodology. It aims to provide an informal bridge between clinicians, charities, and data owners (like CRIS, UKDP, and Biobank) and data science researchers to stimulate and align cutting edge research in this area.
Events
Meetings
We organise monthly meetings (including half-an-hour long invited talks) at the Turing. Meetings are organised and moderated by Jenny Chim, Yue Wu, and Emilio Ferrucci. Please join our mailing list for more updated information.
As a part of AI UK Fringe, we jointly organised a hybrid event with the NLP interest group on AI for Mental Health Monitoring on 28th March 2024.
See here for our previous talks.
Upcoming Events
Meetings
| Date | Time | Presenter | Title |
|---|---|---|---|
| 2025.04.17 | 15:00 | Introduction | |
| 15:05 | Barry Ryan (University of Edinburgh) |
Combining Clinical Embeddings with Multi-Omic Features for Improved Patient Classification and Interpretability in Parkinson’s disease
This talk will demonstrate the integration of Large Language Model (LLM)-derived clinical text embeddings from the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) questionnaire with molecular genomics data to enhance patient classification and interpretability in Parkinson’s disease. By combining genomic modalities encoded using an interpretable biological architecture with a patient similarity network constructed from clinical text embeddings, the approach leverages both clinical and genomic information to provide a robust, interpretable model for disease classification and molecular insights. |
|
| 15:40 | Merve Cerit (Stanford University) |
Working with Screenomes to Build Person-Specific Models of Smartphone Use and Mental Health
In this talk, we will present our work on building person-specific models of smartphone use and mental health using high-frequency Screenome data. We will begin by sharing findings from an intensive longitudinal study that tracked individuals’ digital behaviors and mental health over time, using person-specific analyses to uncover nuanced, within-person associations that are often obscured in group-level studies. Building on this foundation, we will introduce the Media Content Atlas, a computational pipeline that leverages multimodal large language models to analyze and visualize millions of smartphone screenshots. This tool enables content-based clustering, topic modeling, and interactive exploration of digital experiences—offering researchers a scalable way to study how individuals engage with diverse forms of media. Together, these projects advance a new paradigm of personalized digital phenotyping, highlighting how quantitative methods can support tailored insights and interventions for mental health. |
|
| 16:20 | After talks discussion |